Based on prior knowledge, we can analyze the relationship between the feature "education level" and the target variable "subscription to a term deposit". 

The education level of a client may potentially have an impact on their decision to subscribe to a term deposit. Clients with a higher education level might be more likely to subscribe, as they may have a better understanding of the benefits and risks associated with term deposits. On the other hand, clients with a lower education level might be less likely to subscribe, as they may have less financial literacy or awareness of term deposits.

To generate the dictionary, we need to analyze the values of the education level feature for both the "yes" and "no" classes of the target variable.

Here is the dictionary with the specific details:

```json
{
	"no": ["unknown", "primary", "secondary"], 
	"yes": ["tertiary"]
}
```

Explanation:
- For the "no" class (clients who did not subscribe to the term deposit), the possible values of the education level feature are "unknown", "primary", and "secondary". This suggests that clients with these education levels are less likely to subscribe.
- For the "yes" class (clients who subscribed to the term deposit), the possible value of the education level feature is "tertiary". This indicates that clients with this education level are more likely to subscribe.

Please note that since it is specified that values that are hard to predict should not be included in the dictionary, we have excluded those values.